Can We Use Machine Learning to Predict Lower-Extremity Injury in US Special Forces?

Research Paper Title

Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces.

Background

Musculoskeletal injury rates in military personnel remain unacceptably high. Application of machine learning algorithms could be useful in multivariate models to predict injury in this population. The purpose of this study was to investigate if interaction between individual predictors, using a decision tree model, could be used to develop a population-specific algorithm of lower-extremity injury (LEI) risk.

Methods

One hundred forty Air Force Special Forces Operators (27.4 ± 5.0 yr, 177.6 ± 5.8 cm, 83.8 ± 8.4 kg) volunteered for this prospective cohort study. Baseline testing included:

  • Body composition;
  • Isokinetic strength;
  • Flexibility;
  • Aerobic/anaerobic capacity;
  • Anaerobic power; and
  • Landing biomechanics.

To evaluate unilateral landing patterns, subjects jumped off two-feet from a distance (40% of their height) over a hurdle and landing single-legged on a force plate. Medical chart reviews were conducted 365 d post-baseline. χ automatic interaction detection (CHAID) was used, which compares predictor variables to LEI and assigns a population-specific “cut-point” for the most relevant predictors.

Results

Twenty-seven percent of operators (n = 38) suffered LEI. A maximum knee flexion angle difference of 25.1% had the highest association with injury in this population (P = 0.006). Operators with >25.1% differences in max knee flexion angle (n = 13) suffered LEI at a 69.2% rate.

Seven of the 13 Operators with >25.1% difference in max knee flexion angle weighed >81.8 kg, and 100% of those operators suffered LEI (P = 0.047; n = 7). Only 33% of operators with >25.1% difference in max knee flexion angle that weighed <81.8 kg suffered LEI.

Conclusions

This study demonstrated increased risk of LEI over a 365-d period in Operators with greater differences in single-leg landing strategies and higher body mass. The CHAID approach can be a powerful tool to analyse population-specific risk factors for injury, along with how those factors may interact to enhance risk.

Reference

Connaboy, C., Eagle, S.R., Johnson, C.D., Flanagan, S.D., Mi, Q.I. & Nindl, B.C. (2019) Using Machine Learning to Predict Lower-Extremity Injury in US Special Forces. Medicine and Science in Sport and Exercise. 51(5), pp.1073-1079. doi: 10.1249/MSS.0000000000001881.

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